Why is the Fourier transform of a sinusoid assumed as this?

In summary, structured illumination microscopy is a method where an object is illuminated with a sinusoidal pattern, resulting in a Fourier transform of the image with three replicas of the object's Fourier transform. The additional replica at the origin comes from the square-to-double-argument trigonometric identity. This method is commonly used in 2D surface analysis and the intensity of the signal is considered as the square of the signal.
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Hello everyone.

I'm trying to better understand structured illumination microscopy and in the literature, I keep coming across bits of text like this.
Structured illumination is one such method where the object is illuminated with a sinusoidal pattern instead of the conventional uniform illumination. The Fourier transform of the intensity of a sinusoid is three impulses—one at the origin and the other two at the positive and negative spatial frequency of the sinusoid. Therefore, when a sinusoidal illumination is incident on an object, the Fourier transform of the image consists of three replicas of the object Fourier transform, each centered at one of the three impulses.

Source: http://www.optics.rochester.edu/workgroups/fienup/PUBLICATIONS/SAS_JOSAA09_PhShiftEstSupRes.pdf

From Fourier analysis, if I take the Fourier transform ##X(f)## of a time-varying function ##x(t)## that is a cosine, I get a pair of delta functions (quick derivation below).
\begin{align*}
X(f) & = \int_{-\infty}^{\infty} x(t)\exp(-i2\pi f t)\; dt\\
x(t) &= \cos(2 \pi f_0 t) = \frac{1}{2}\left[\exp(i2\pi f_0 t) + \exp(-i2\pi f_0 t)\right]\\
X(f) &= \int_{-\infty}^{\infty} \frac{1}{2}\left[\exp(i2\pi f_0 t) + \exp(-i2\pi f_0 t)\right]\exp(-i2\pi f t)\; dt\\
&= \frac{1}{2}[\int_{-\infty}^{\infty} \exp(i2\pi f_0 t)\exp(-i2\pi f t)\; dt + \int_{-\infty}^{\infty} \exp(-i2\pi f_0 t)\exp(-i2\pi f t)\; dt\\
&= \frac{1}{2}[\mathcal{F}\left\{\exp(i2\pi f_0 t)\right\} + \mathcal{F}\left\{\exp(-i2\pi f_0 t)\right\}]\\
\mathcal{F}\left\{\exp(i2\pi f_0 t)\right\} &= \delta(f-f_0)\\
\mathcal{F}\left\{\exp(-i2\pi f_0 t)\right\} &= \delta(f+f_0)\\
\therefore X(f) &= \frac{1}{2}[\delta(f-f_0) + \delta(f+f_0)]
\end{align*}
But the paper says that I should be getting three impulses. One at the origin, and the two I have detailed above. Where does the one at the origin come from?

My only hunch so far is it might have something to do with the fact that this derivation I just did was in 1D, and what they are describing is in 2D (a surface). Of course, the other minor difference is that they are describing spatial frequency and I am describing temporal frequency, but replacing ##t## with ##x## and ##f## with a scaled ##k## (spatial frequency) isn't a big deal.

Any tips?
 
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  • #2
An aside: the argument for the exp function should have "i" in it. [itex]2\pi ift[/itex] etc.
 
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  • #3
mathman said:
An aside: the argument for the exp function should have "i" in it. [itex]2\pi ift[/itex] etc.
You're right. I will fix that.
 
  • #4
The word "intensity" usually means the square of the signal - from a quick glance at the paper they are indeed considering the square. Look at equation (1) in the paper, and note
[tex]
\cos^2(x) = \frac{1}{2}(1 + \cos(2x))
[/tex]
 
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  • #5
jasonRF said:
The word "intensity" usually means the square of the signal - from a quick glance at the paper they are indeed considering the square. Look at equation (1) in the paper, and note
[tex]
\cos^2(x) = \frac{1}{2}(1 + \cos(2x))
[/tex]

That's exactly it. The "DC" term comes from the square-to-double-argument trigonometric identity. Got it. Thank you very much! The rest of it is straightforward. :)
 

1. Why is the Fourier transform of a sinusoid assumed as a delta function?

The Fourier transform of a sinusoid is assumed as a delta function because it represents a pure frequency component. A sinusoid is an infinite series of sinusoidal functions at different frequencies, and the Fourier transform simplifies this to a single frequency component at the same amplitude.

2. How does the Fourier transform of a sinusoid relate to the complex exponential function?

The Fourier transform of a sinusoid is closely related to the complex exponential function because a sinusoidal signal can be expressed as a sum of complex exponential functions. This relationship allows us to use the Fourier transform to analyze sinusoidal signals and their frequency components.

3. Why is the Fourier transform of a sinusoid a continuous function?

The Fourier transform of a sinusoid is a continuous function because it represents the frequency content of a signal as a function of frequency. The transform is continuous because it considers all possible frequencies, including those between the discrete frequency components of the signal.

4. How is the Fourier transform of a sinusoid used in signal processing?

The Fourier transform of a sinusoid is used in signal processing to analyze signals in the frequency domain. It allows us to understand the frequency content of a signal and filter out unwanted frequency components. It is also used to convert signals between the time and frequency domains.

5. Why is the Fourier transform of a sinusoid important in engineering and science?

The Fourier transform of a sinusoid is important in engineering and science because it provides a powerful tool for analyzing and understanding signals. It allows us to break down complex signals into simpler frequency components and study their behavior. This is essential in fields such as signal processing, communication systems, and image processing.

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